Optimal car-following control for intelligent vehicles using online road-slope approximation method

The design of a car-following control system is a multiobjective optimization problem that involves issues in rider safety, ride comfort, and fuel economy. This study proposes a hierarchical design of optimal car-following control where the system is intuitively split into two subsystems with different dynamic properties. Specifically, the high-level subsystem is a linear car-following system with a measurable disturbance of the preceding vehicle's acceleration, while the low-level subsystem is a nonlinear acceleration-tracking system with an unmeasurable road slope. In the design of optimal car-following control, the measurable disturbance of the preceding vehicle's acceleration is considered from a theoretical perspective, and the unmeasurable road slope is estimated by a novel engineering-oriented approximation method to reduce the influence of driveline oscillation. The performance of the proposed optimal control scheme is evaluated through simulation and real-vehicle experiments, which show that the proposed control algorithm provides a satisfactory road-slope approximation accuracy and that the car-following performance of the proposed optimal control system is better than that of a factory-installed adaptive cruise controller.

Automated summary

This study proposes a hierarchical design of optimal car-following control system, which splits the system into high-level and low-level subsystems to address issues such as preceding vehicle acceleration disturbance and road slope. The performance of the proposed optimal control scheme is evaluated through simulation and real-vehicle experiments, showing superior car-following performance compared to a factory-installed adaptive cruise controller.